This paper proposes a system to automatically perform classification and data cleaning on location images in a database contributed by arbitrary users. Since human inspection is not feasible for large-scale databases, the ability to detect incorrect scenes uploaded by users is very important to maintain the correctness of the database. In this work, we compare different feature extractors using deep convolutional networks trained by massive datasets. Also, a detector is designed to identify incorrect scenes that can overcome the challenges of large intra-cluster distances. The experiments have validated the effectiveness of the proposed approach on a very challenging dataset.